ure and further examine the impact of catastrophic interpretations of suicidal thoughts. Highlights Created a new measure for sensitivity to thoughts of suicide and wanting to die. Suicidal cognition concerns associated with suicidal ideation in the past two weeks. Suicidal cognition concerns associated with lifetime worst point suicidal ideation. Suicidal cognition concerns moderated AS cognitive concerns and ideation relation.BackgroundImmune checkpoint inhibitors (ICIs) have been increasingly used in cancer treatment, and a subset of patients undergo pseudoprogression. Recognizing the incidence of pseudoprogression is critical for clinical practice.PurposeTo evaluate by systematic review and meta-analysis the incidence of pseudoprogression in cancer treatment with ICIs, and compare the incidence according to response criteria, tumor types, and immunotherapeutic agents.Materials and MethodsMedline and Embase were searched to identify relevant studies published before December 31, 2018. Clinical trials, post hoc analysis of clinical trials, and prospective studies on ICI treatment in patients with malignant solid tumors were included. Pooled incidence of pseudoprogression for all included studies, per definition of pseudoprogression, cancer type, and drug type, was obtained by random-effects models with inverse variance weighting model.ResultsSeventeen studies with 3402 patients were analyzed. The pooled incidence of pseudoprogresses the need for uniform criteria of pseudoprogression for solid tumors.© RSNA, 2020Online supplemental material is available for this article.See also the article by Dodd and MacDermott in this issue.Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study included 386 patients (mean age, 52 years ± 16 [standard deviation]; 226 men) with astrocytomas diagnosed with histopathologic analysis who underwent dynamic susceptibility contrast (DSC)-enhanced and DCE MRI preoperatively from April 2010 to January 2018. The AIF was obtained from each sequence AIF obtained from DSC-enhanced MRI (AIFDSC) and AIF measured at DCE MRI (AIFDCE). The model was trained to translate AIFDCE into AIFDSC, and after training, outputted neural-network-generated AIF (AIFgenerated DSC) with input AIFintraclass correlation coefficients with AIFgenerated DSC than AIFDCE (0.77 vs 0.29, P less then .001; 0.68 vs 0.42, P = .003; and 0.66 vs 0.45, P = .01, respectively. Conclusion A deep learning algorithm improved both reliability and diagnostic performance of MRI pharmacokinetic parameters for differentiating astrocytoma grades. ©?RSNA, 2020 Online supplemental material is available for this article.A persistent and growing challenge to the field of neuropsychology is the disconnect between (a) the increasingly culturally/linguistically diverse populations in need of clinical and research evaluations and (b) a neuropsychology workforce and 'toolkit' of validated instruments and norms that remain generally ill-prepared to address these needs. This disconnect threatens the future clinical utility and professional viability of the field, and may at least in part be related to white privilege. https://www.selleckchem.com/products/4-Methylumbelliferone(4-MU).html This commentary describes a qualitative examination of white privilege in neuropsychology, its implications for the field, and recommendations to move forward.
Utilizing McIntosh's paradigm of "unpacking the invisible knapsack of white privilege," this author (a non-Hispanic White, Spanish-English bilingual man) conducted an idiographic, qualitative examination of ways in which non-Hispanic White neuropsychologists may experience unearned and largely invisible (unexamined) privilege.
The present examination suggeufficient systemic response to its longstanding challenges related to workforce demographics and psychometric instrumentation. To ensure future clinical utility and professional viability, it is imperative that neuropsychology as a field, and particularly the non-Hispanic White majority of its membership and organizational leaders, unpack its invisible knapsack of privilege and acknowledge the ways in which such privilege can insidiously compromise individual and systemic responses to the ongoing crisis of insufficient workforce characteristics, psychometric tools, and empirical research basis to address increasing patient diversity and neuropsychological health care disparities.The secretin receptor (SCTR), a prototypical class B G protein-coupled receptor (GPCR), exerts its effects mainly by activating Gαs proteins upon binding of its endogenous peptide ligand secretin. SCTRs can be found in a variety of tissues and organs across species, including the pancreas, stomach, liver, heart, lung, colon, kidney, and brain. Beyond that, modulation of SCTR-mediated signaling has therapeutic potential for the treatment of multiple diseases, such as heart failure, obesity, and diabetes. However, no ligands other than secretin and its peptide analogs have been described to regulate SCTRs, probably due to inherent challenges in family B GPCR drug discovery. Here we report creation of a testing funnel that allowed targeted detection of SCTR small-molecule activators. Pursuing the strategy to identify positive allosteric modulators (PAMs), we established a unique primary screening assay employing a mixture of three orthosteric stimulators that was compared in a screening campaign testing 12,000 small-molecule compounds. Beyond that, we developed a comprehensive set of secondary assays, such as a radiolabel-free target engagement assay and a NanoBiT (NanoLuc Binary Technology)-based approach to detect β-arrestin-2 recruitment, all feasible in a high-throughput environment as well as capable of profiling ligands and hits regarding their effect on binding and receptor function. This combination of methods enabled the discovery of five promising scaffolds, four of which have been validated and further characterized with respect to their allosteric activities. We propose that our results may serve as starting points for developing the first in vivo active small molecules targeting SCTRs.